PCA NN Based Classifier For Liver Diseases from Ultrasonic Liver Images

This research aims at developing an optimal neural network based DSS, which is aimed at precise and reliable diagnosis of chronic active hepatitis (CAH) and cirrhosis (CRH). The principal component analysis neural network is designed scrupulously for classification of these diseases. The neural network is trained by eight quantified texture features, which were extracted from five different region of interests (ROIs) uniformly distributed in each B-mode ultrasonic image of normal liver (NL), chronic active hepatitis (CAH) and cirrhosis (CRH). The proposed PCA NN classifier is the most efficient learning machine that is able to classify all three cases of diffused liver with average classification accuracy of 95.23%; 6 cases of cirrhosis out of 7 (6/7), all 7 cases of chronic active hepatitis (7/7) and all 15 cases of normal liver (15/15).